write Ne_LD_sp results to a file

gistfile1.py

def splitCalcMerge(pop, param):
    # Calculate population sizes to help with splitting (see below).
    pop_size_croatia = pop.subPopSize(0)
    pop_size_slovenia = pop.subPopSize(1)
    pop_sizes = {"1":pop_size_croatia, "3":pop_size_slovenia}

    # Split population into two. First subpopulation is the main one minus the
    # sample size (ss).
    sim.splitSubPops(pop = pop, subPops = 0, sizes = [pop_size_croatia - param["croatia"], param["croatia"]])
    sim.splitSubPops(pop = pop, subPops = 2, sizes = [pop_size_slovenia - param["slovenia"], param["slovenia"]])

    sim.stat(pop = pop, popSize = "subPopSize")
    sim.stat(pop = pop, effectiveSize = sim.ALL_AVAIL, vars = "Ne_LD_sp")

    # Define levels in "x", namely mean, lower and upper confidence intervals.
    x_name = ["fit", "lci", "uci"]

    # Construct a dictionary that can be read in columns:
    #               gen, pop_num, pop_name, x, 0.0, 0.01, 0.02, 0.05, actual_size
    # generation ____/      /      /       /   /     /     /     /      /
    # population num ______/      /       /   /     /     /     /      /
    # population name ___________/       /   /     /     /     /      /
    # variable designation _____________/   /     /     /     /      /
    # value without a cutoff ______________/     /     /     /      /
    # value at cutoff of 0.01 __________________/     /     /      /
    # value at cutoff of 0.02 _______________________/     /      /
    # value at cutoff of 0.05 ____________________________/      /
    # actual subpopulation size ________________________________/

# Output results into a results.txt file.
    with open("results.txt", mode = "a") as rf: #rf = result file

        # Run only through the "sample" population.
        for p in [1, 3]:
            data = []
            for x in range(3): # fit, lci, uci
                v = []
                for y in [0.0, 0.01, 0.02, 0.05]:
                    v.append(pop.dvars().subPop[p]["Ne_LD"][y][x])
                data.append(v)

                to_print = "{gen}, {pop}, {popname}, {x}, {data0}, {data01}, {data02}, {data05} {actual_size}".format(
                    gen = pop.dvars().gen, pop = pop.subPopNames()[p], popname = p, x = x_name[x], data0 = data[x][0],
                    data01 = data[x][1], data02 = data[x][2], data05 = data[x][3], actual_size = pop_sizes[str(p)])
                print >> rf, to_print

    sim.mergeSubPops(pop = pop, subPops = [0, 1], name = "croatia")
    sim.mergeSubPops(pop = pop, subPops = [1, 2], name = "slovenia")

    return True